S&P 500 Latest Stocks Analysis¶

=====================================

In this notebook, we will delve into the most recent 2019-2024 stocks from the index

TABLE OF CONTENTS

Data Sources¶
  • Yahoo Finance
  • S&P 500 data

Analysis of Most Traded Stocks¶

==================================

Key Analyses¶

  • Volume Analysis
  • Closing Price Analysis

Comparative Analysis of Top Tech Stocks¶

==========================================

  • Selection of Top 6 Tech Stocks in Software-Infrastructure Industry by Market Cap
  • Mean Price Analysis
  • Volatility Analysis
  • Risk-Return Analysis
  • Moving Average Analysis
    • Golden Cross and Death Cross Identification

Fundamental Analysis¶

=====================

  • Institutional Ownership Analysis • Top 10 Institutional Holders for Various Related Stocks
  • Earnings Per Share (EPS) Analysis
  • Price-to-Earnings (PE) Ratio Analysis
  • Piotroski F-Score Analysis
  • • Profitability Metrics • Return on Assets (ROA) • Return on Equity (ROE)
  • • Leverage Metrics • Long-term Debt to Equity Ratio • Interest Coverage Ratio
  • • Operating Efficiency Metrics • Asset Turnover Ratio • Operating Cash Flow Margin
In [1]:
import pandas as pd 
from datetime import datetime, timedelta
import warnings
import yfinance as yf
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta 
from scipy import stats
import matplotlib.dates as mdates
from matplotlib.colors import LinearSegmentedColormap
from plotly import tools
import plotly.tools as tls
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, plot, iplot
import statsmodels.api as sm
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
In [2]:
df1=pd.read_csv(r"C:\Users\PMLS\Downloads\sAND P TOP 5000 COAMPNIES NAMES\sp500_stocks.csv")
df1.head()
Out[2]:
Date Symbol Adj Close Close High Low Open Volume
0 2010-01-04 MMM 44.016724 69.414719 69.774246 69.122070 69.473244 3640265.0
1 2010-01-05 MMM 43.741020 68.979935 69.590302 68.311035 69.230766 3405012.0
2 2010-01-06 MMM 44.361343 69.958191 70.735786 69.824417 70.133781 6301126.0
3 2010-01-07 MMM 44.393166 70.008362 70.033447 68.662209 69.665550 5346240.0
4 2010-01-08 MMM 44.705982 70.501671 70.501671 69.648827 69.974915 4073337.0

Data Cleaning¶

In [3]:
df1['Date'] = pd.to_datetime(df1['Date']) # Converted to date time 

now_time = datetime.now()
five_years_ago = now_time - timedelta(days=5*365)

df = df1[df1['Date'] >= five_years_ago]
df = df.reset_index(drop=True)  
  • Lets Check if Anyy of the Dates are Missing
In [4]:
df['Date'] = pd.to_datetime(df['Date'])

# Define the start and end dates
start_date = df['Date'].min()  # Use the minimum date from the DataFrame
end_date = pd.to_datetime('2024-09-04')

# Generate a date range from start_date to end_date
full_date_range = pd.date_range(start=start_date, end=end_date)

# Extract the unique dates from the 'Date' column in df
available_dates = pd.to_datetime(df['Date'].unique())

# Find the missing dates by comparing the full range with available dates
missing_dates = full_date_range.difference(available_dates)

# Get the count of missing dates
missing_count = len(missing_dates)

# Print the count of missing dates
print(f"Count of Missing Dates: {missing_count}")


num_to_display = min(missing_count, 5)  # Here I will only show the first 5 missing dates  
for missing_date in missing_dates[:num_to_display]:
    print(f"{missing_date.strftime('%d %B %Y')} is missing")
Count of Missing Dates: 557
19 October 2019 is missing
20 October 2019 is missing
26 October 2019 is missing
27 October 2019 is missing
02 November 2019 is missing

While This Number (592) May Seem Small Here,¶

Note: The period from September 9, 2019, to September 9, 2024, contains exactly 1,827 days.

  • This actually means 32.4% of our data is missing, which is a significant portion that can greatly affect our results.
  • To address this issue, we have written a script that will fetch the missing data for these specific dates directly from the YFinance API, based on the stock list we will be providing.
In [5]:
df=df.rename(columns={"Symbol":"ticks"})
df.columns = df.columns.str.lower()

-- Function to fetch the missing dates

In [6]:
def fill_missing_dates(df, tickers):
    df['date'] = pd.to_datetime(df['date'], utc=False)

    start_date = df['date'].min()
    end_date = df['date'].max()
    full_date_range = pd.date_range(start=start_date, end=end_date)
    
    # Create a dictionary to store data for each ticker
    ticker_data = {ticker: df[df['ticks'] == ticker] for ticker in tickers}
    
    for ticker in tickers:
        ticker_df = ticker_data[ticker]
        
        # Find missing dates for this ticker
        available_dates = pd.to_datetime(ticker_df['date'].unique())
        missing_dates = full_date_range.difference(available_dates)
        
        if len(missing_dates) > 0:
            print(f"Fetching {len(missing_dates)} missing dates for {ticker}")
            
            # Fetch missing data from Yahoo Finance
            yf_ticker = yf.Ticker(ticker)
            missing_data = yf_ticker.history(start=missing_dates.min(), end=missing_dates.max() + timedelta(days=1))
            
            # Process the fetched data
            missing_data = missing_data.reset_index()
            missing_data = missing_data.rename(columns={
                'Date': 'date',
                'Open': 'open',
                'High': 'high',
                'Low': 'low',
                'Close': 'close',
                'Adj Close': 'adj close',
                'Volume': 'volume'
            })
            missing_data['ticks'] = ticker
            
            # Add any missing columns with NaN values
            for col in df.columns:
                if col not in missing_data.columns:
                    missing_data[col] = pd.NA
            
            # Convert 'date' column to tz-naive (remove timezone info) for both dataframes
            ticker_df['date'] = pd.to_datetime(ticker_df['date']).dt.tz_localize(None)
            missing_data['date'] = pd.to_datetime(missing_data['date']).dt.tz_localize(None)

            # Combine the missing data with the existing data
            ticker_data[ticker] = pd.concat([ticker_df, missing_data]).sort_values('date')
    
    # Combine all ticker data back into a single dataframe
    updated_df = pd.concat(ticker_data.values()).sort_values(['ticks', 'date'])
    updated_df = updated_df.reset_index(drop=True)
    
    return updated_df

# This is how we will be using this Function 
tickers = ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']
updated_df = fill_missing_dates(df, tickers)
Fetching 557 missing dates for NVDA
Fetching 557 missing dates for TSLA
Fetching 557 missing dates for AAPL
Fetching 557 missing dates for AMZN
Fetching 557 missing dates for F
Fetching 557 missing dates for AMD
Fetching 557 missing dates for BAC
Fetching 557 missing dates for T
Fetching 557 missing dates for AAL
Fetching 557 missing dates for CCL

Checking Our Dates again¶

In [7]:
updated_df.isnull().sum()
Out[7]:
date                0
ticks               0
adj close       12240
close               0
high                0
low                 0
open                0
volume              0
Dividends       12300
Stock Splits    12300
dtype: int64
  • Now we have Now Data Missing , at least for the Columns we will be using
In [8]:
new_df = updated_df
mean_dict = {}

# find average of volume traded over a period of time using for loops
for key in new_df['ticks'].unique():
    value = new_df[new_df['ticks'] == key ]['volume'].mean()
    mean_dict[key]= value

print("Length of the mean of ticks dictionary:", len(mean_dict))

# convert dict to pandas dataframe
avaerage_s = pd.Series(mean_dict)
top10_s = avaerage_s.sort_values(ascending=False)[:10]

print("Top 10 company tickers with highest average traded stock volume:\n", top10_s.index)
Length of the mean of ticks dictionary: 10
Top 10 company tickers with highest average traded stock volume:
 Index(['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL'], dtype='object')
In [ ]:
 
  • F is Ford Motor Company
  • T is AT&T Inc.
In [9]:
tickers = ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']
updated_df = fill_missing_dates(df, tickers)
updated_df
Fetching 557 missing dates for NVDA
Fetching 557 missing dates for TSLA
Fetching 557 missing dates for AAPL
Fetching 557 missing dates for AMZN
Fetching 557 missing dates for F
Fetching 557 missing dates for AMD
Fetching 557 missing dates for BAC
Fetching 557 missing dates for T
Fetching 557 missing dates for AAL
Fetching 557 missing dates for CCL
Out[9]:
date ticks adj close close high low open volume Dividends Stock Splits
0 2019-10-15 AAL 28.074911 28.270000 28.370001 27.280001 27.629999 7019200.0 NaN NaN
1 2019-10-16 AAL 27.856426 28.049999 28.809999 28.030001 28.330000 6353300.0 NaN NaN
2 2019-10-17 AAL 27.737255 27.930000 28.440001 27.850000 28.309999 6709600.0 NaN NaN
3 2019-10-18 AAL 28.025253 28.219999 28.320000 27.709999 27.799999 5689100.0 NaN NaN
4 2019-10-21 AAL NaN 28.422495 28.710493 28.233806 28.313253 6746400.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ...
24535 2024-08-29 TSLA NaN 206.279999 214.889999 205.970001 209.800003 62308800.0 0.0 0.0
24536 2024-08-30 TSLA 214.110001 214.110001 214.570007 207.029999 208.630005 63370600.0 NaN NaN
24537 2024-08-30 TSLA NaN 214.110001 214.570007 207.029999 208.630005 63370600.0 0.0 0.0
24538 2024-09-03 TSLA 210.600006 210.600006 219.899994 209.639999 215.259995 76500900.0 NaN NaN
24539 2024-09-04 TSLA 219.410004 219.410004 222.220001 210.619995 210.759995 80181916.0 NaN NaN

24540 rows × 10 columns

In [10]:
def subdataframe(df, tick):
    
    # top 10 ticks
    ticks = list(top10_s.index)
    
    assert tick in ticks, """Stock tick does not belong to top 10 stocks by trade volume, please try any of these:\n
    ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']"""
    
    ndf = new_df[new_df['ticks'] == tick]
    return ndf
In [11]:
sp500Cmp_info_df = pd.read_csv('C:/Users/PMLS/Downloads/sAND P TOP 5000 COAMPNIES NAMES/sp500_companies.csv')
Top10HigestTradeNVD=['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']
info_df=pd.DataFrame({'tick':Top10HigestTradeNVD})
info_df
Out[11]:
tick
0 NVDA
1 TSLA
2 AAPL
3 AMZN
4 F
5 AMD
6 BAC
7 T
8 AAL
9 CCL
In [12]:
Final_info = pd.merge(info_df, sp500Cmp_info_df, how='left', left_on='tick', right_on='Symbol')
Final_info =Final_info[['tick','Shortname','Sector','Industry']]
Final_info
Out[12]:
tick Shortname Sector Industry
0 NVDA NVIDIA Corporation Technology Semiconductors
1 TSLA Tesla, Inc. Consumer Cyclical Auto Manufacturers
2 AAPL Apple Inc. Technology Consumer Electronics
3 AMZN Amazon.com, Inc. Consumer Cyclical Internet Retail
4 F Ford Motor Company Consumer Cyclical Auto Manufacturers
5 AMD Advanced Micro Devices, Inc. Technology Semiconductors
6 BAC Bank of America Corporation Financial Services Banks - Diversified
7 T AT&T Inc. Communication Services Telecom Services
8 AAL American Airlines Group, Inc. Industrials Airlines
9 CCL Carnival Corporation Consumer Cyclical Travel Services
In [26]:
# Update company dict for your top 10 companies
company_dict = {
    'NVDA': 'NVIDIA', 
    'TSLA': 'Tesla', 
    'AAPL': 'Apple', 
    'AMZN': 'Amazon', 
    'F': 'Ford Motor Company', 
    'AMD': 'Advanced Micro Devices', 
    'BAC': 'Bank of America', 
    'T': 'AT&T', 
    'AAL': 'American Airlines', 
    'CCL': 'Carnival Corporation'
}

# Create sub-dataframes for each company
nvda_df = subdataframe(new_df, 'NVDA')
tsla_df = subdataframe(new_df, 'TSLA')
aapl_df = subdataframe(new_df, 'AAPL')
amzn_df = subdataframe(new_df, 'AMZN')
f_df = subdataframe(new_df, 'F')
amd_df = subdataframe(new_df, 'AMD')
bac_df = subdataframe(new_df, 'BAC')
t_df = subdataframe(new_df, 'T')
aal_df = subdataframe(new_df, 'AAL')
ccl_df = subdataframe(new_df, 'CCL')

# Define the function to calculate daily return and company name
def dailyfunc(df):
    df['daily return'] = ((df['close'] - df['open']) / df['open']) * 100
    df.style.format('{:.2f}%', subset='daily return')
    df['daily_mean'] = (df['open'] + df['close'] + df['high'] + df['low']) / 4
    df['co_name'] = company_dict[df['ticks'].unique()[0]]
    return df

# Apply dailyfunc to each company's dataframe
nvda_df = dailyfunc(nvda_df)
tsla_df = dailyfunc(tsla_df)
aapl_df = dailyfunc(aapl_df)
amzn_df = dailyfunc(amzn_df)
f_df = dailyfunc(f_df)
amd_df = dailyfunc(amd_df)
bac_df = dailyfunc(bac_df)
t_df = dailyfunc(t_df)
aal_df = dailyfunc(aal_df)
ccl_df = dailyfunc(ccl_df)

# Print the start and end date for each company One more for Confioatnos 
print('\t\tStart Date\t\t\t\t\tEnd Date')
print(f"NVDA\t\t{nvda_df['date'].min()}\t\t\t{nvda_df['date'].max()}")
print(f"TSLA\t\t{tsla_df['date'].min()}\t\t\t{tsla_df['date'].max()}")
print(f"AAPL\t\t{aapl_df['date'].min()}\t\t\t{aapl_df['date'].max()}")
print(f"AMZN\t\t{amzn_df['date'].min()}\t\t\t{amzn_df['date'].max()}")
print(f"F\t\t{f_df['date'].min()}\t\t\t{f_df['date'].max()}")
print(f"AMD\t\t{amd_df['date'].min()}\t\t\t{amd_df['date'].max()}")
print(f"BAC\t\t{bac_df['date'].min()}\t\t\t{bac_df['date'].max()}")
print(f"T\t\t{t_df['date'].min()}\t\t\t{t_df['date'].max()}")
print(f"AAL\t\t{aal_df['date'].min()}\t\t\t{aal_df['date'].max()}")
print(f"CCL\t\t{ccl_df['date'].min()}\t\t\t{ccl_df['date'].max()}")
		Start Date					End Date
NVDA		2010-01-04			2024-09-04
TSLA		2010-01-04			2024-09-04
AAPL		2010-01-04			2024-09-04
AMZN		2010-01-04			2024-09-04
F		2010-01-04			2024-09-04
AMD		2010-01-04			2024-09-04
BAC		2010-01-04			2024-09-04
T		2010-01-04			2024-09-04
AAL		2010-01-04			2024-09-04
CCL		2010-01-04			2024-09-04
  • Here Our Start Data and End Data for all the Stocks are Correct , Ensuring Data Validation

Technical Analysis¶

  • Lets Start from Finding The All time High Price of Our Most Traded Stocks
In [14]:
def plot_closing_stock_prices(dfs, ncols=2):
    # Calculate the number of rows needed
    nrows = (len(dfs) + ncols - 1) // ncols
    
    # Create the figure and axes for the subplots
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 5 * nrows), facecolor='#f4f4f4')
    fig.subplots_adjust(hspace=0.4, wspace=0.3)
    
    for ax, df in zip(axes.flat, dfs):
        # Calculate the highest stock price and the corresponding date
        high = df['close'].max()
        datetime = df[df['close'] == high]['date'].values[0]  

        # Define color based on the stock ticker
        if df['ticks'].unique()[0] in ['GE', 'F']:
            facecolor = '#ed615c'
        else:
            facecolor = '#4bd659'
        
        # Plot the data
        ax.plot(df['date'], df['close'], color='#0f2113')
        ax.set_title(f"{df['co_name'].unique()[0]} Stock Price", fontsize=16, fontweight='bold')
        ax.set_xlabel("Date", fontsize=12)
        ax.set_ylabel("Daily Closing Stock Price", fontsize=12)
        ax.set_facecolor('#ffffff')  # Set background color for each subplot
        
        # Add annotation for the highest stock price
        ax.annotate(
            f"All time high price during\nfive year period\nwas ${high:.2f}",
            xy=(datetime, high),
            xytext=(datetime, high - 0.1 * high),
            bbox=dict(boxstyle="round", facecolor='#f5d3bf', edgecolor='#d0d5db'),
            arrowprops=dict(facecolor='#f0190a', headlength=25, shrink=0.1)
        )

    # Hide any unused subplots
    for ax in axes.flat[len(dfs):]:
        ax.set_visible(False)

    plt.show()

# Call the function with your dataframes
plot_closing_stock_prices([nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df])
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Trade Volume Analysis¶

Look at the exact numbers of volume of how much these stocks were traded

  • Identify when the trade volume was highest and at what value
In [15]:
def create_trade_volume_grid_single_row(dataframes):
    # Set the number of columns to be equal to the number of dataframes
    cols = len(dataframes)

    # Create subplot grid with 1 row and as many columns as there are dataframes
    fig = make_subplots(rows=1, cols=cols, subplot_titles=[df['co_name'].unique()[0] for df in dataframes])

    for i, df in enumerate(dataframes, 1):
        # All plots will be on row 1, so just adjust the column
        row = 1
        col = i

        # Calculate statistics
        ave_y = df['volume'].mean()
        max_y = df['volume'].max()
        min_y = df['volume'].min()
        max_date = df[df['volume'] == max_y]['date'].values[0]

        # Add trace for volume
        fig.add_trace(
            go.Scatter(x=df['date'], y=df['volume'], name='Volume', line=dict(color='#00CED1')),
            row=row, col=col
        )

        # Add horizontal lines for max, min, and average
        fig.add_hline(y=max_y, line_dash="dash", line_color="red", row=row, col=col)
        fig.add_hline(y=min_y, line_dash="dash", line_color="green", row=row, col=col)
        fig.add_hline(y=ave_y, line_dash="dash", line_color="yellow", row=row, col=col)

        # Add vertical line for max volume date
        fig.add_vline(x=max_date, line_dash="dash", line_color="purple", row=row, col=col)

        # Update axes
        fig.update_xaxes(title_text="Date", row=row, col=col)
        fig.update_yaxes(title_text="Volume", row=row, col=col)

    # Update layout
    fig.update_layout(height=300, width=400*cols, title_text="Stock Trade Volumes",
                      showlegend=False, template="plotly_dark")

    return fig

# Example usage
dataframes = [nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df]
fig = create_trade_volume_grid_single_row(dataframes)
fig.show()
In [16]:
list_df = [nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df]

# loop through the the list_df to find mini and maxi of each stocks 
mini = [df[df['date'] == df['date'].min()]['close'].values.item() for df in list_df]
maxi = [df[df['date'] == df['date'].max()]['close'].values.item() for df in list_df]

# find list of abosolute difference between both stock price
diff = np.array(maxi) - np.array(mini)

# find the percentage growth
growth = (diff/mini)*100
growth_list = growth.tolist()
co_name_list = [df['co_name'].unique()[0] for df in list_df]

# visualize the growth of the stocks
fig, ax = plt.subplots(figsize=(13,7))
ax.barh(y=co_name_list, width=growth_list, height=0.9, color=['#4bd659','#4bd659','#4bd659','#4bd659','#4bd659',
                                                             '#4bd659','#4bd659','#ed615c','#ed615c','#ed615c'],
       edgecolor='#713ae8')
for p in ax.patches:
    ax.annotate(f'{round(p.get_width(),2)}%', (p.get_width()+15, p.get_y() +0.3))
ax.set_xlabel('Percentage growth in stock price')
ax.set_ylabel('Name of companies')
ax.set_title("Growth in stock price over a period of 5 years")
plt.show()
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Comparitive Analysis of of Top Tech Stocks in the Index¶

Now we are going to Move onto form our Most traded stocks To A comparitive Analysis of Top Stocks in the Tech Sectory ,

  • To do this , Firstly we filtred out Top 5 tech stocks from the Orignal Dataframe Software - Infrastructure industry
In [17]:
# function to return top 10 sub dataframe
def subdataframe(df, tick):
    
    # top 10 ticks
    ticks = list(top10_s.index)
    
    assert tick in ticks, """Stock tick does not belong to top 10 stocks by trade volume, please try any of these:\n
    ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']"""
    
    ndf = new_df[new_df['ticks'] == tick]
    return ndf
In [18]:
ab = sp500Cmp_info_df[sp500Cmp_info_df['Industry'] == 'Software - Infrastructure']
ab=ab.sort_values(by='Marketcap',ascending=False)
ab=ab.head(6)
ab
Out[18]:
Exchange Symbol Shortname Longname Sector Industry Currentprice Marketcap Ebitda Revenuegrowth City State Country Fulltimeemployees Longbusinesssummary Weight
1 NMS MSFT Microsoft Corporation Microsoft Corporation Technology Software - Infrastructure 408.90 3039369887744 1.294330e+11 0.152 Redmond WA United States 228000.0 Microsoft Corporation develops and supports so... 0.059346
20 NYQ ORCL Oracle Corporation Oracle Corporation Technology Software - Infrastructure 140.75 387887300608 2.122700e+10 0.033 Austin TX United States 159000.0 Oracle Corporation offers products and service... 0.007574
28 NMS ADBE Adobe Inc. Adobe Inc. Technology Software - Infrastructure 575.25 255065849856 7.844000e+09 0.102 San Jose CA United States 29945.0 Adobe Inc., together with its subsidiaries, op... 0.004980
87 NMS PANW Palo Alto Networks, Inc. Palo Alto Networks, Inc. Technology Software - Infrastructure 346.15 112533364736 9.601666e+08 0.121 Santa Clara CA United States NaN Palo Alto Networks, Inc. provides cybersecurit... 0.002197
130 NMS SNPS Synopsys, Inc. Synopsys, Inc. Technology Software - Infrastructure 477.53 73355296768 1.652390e+09 0.127 Sunnyvale CA United States 20300.0 Synopsys, Inc. provides electronic design auto... 0.001432
149 NMS CRWD CrowdStrike Holdings, Inc. CrowdStrike Holdings, Inc. Technology Software - Infrastructure 259.32 63566077952 1.874900e+08 0.317 Austin TX United States 9219.0 CrowdStrike Holdings, Inc. provides cybersecur... 0.001241
In [27]:
new_df=pd.read_csv(r"C:\Users\PMLS\Downloads\sAND P TOP 5000 COAMPNIES NAMES\sp500_stocks.csv")
new_df=new_df.rename(columns={"Symbol":"ticks"})
new_df.columns = new_df.columns.str.lower()
In [28]:
company_dict = {
    'MSFT': 'Microsoft',
    'ORCL': 'Oracle Corporation',
    'ADBE': 'Adobe Inc.',
    'PANW': 'Palo Alto Networks',
    'SNPS': 'Synopsys Inc.',
    'CRWD': 'CrowdStrike Holdings'
}
In [29]:
def subdataframe(df, tick, *ticks):
    ticks_list = list(ticks)
    assert tick in ticks_list, f"Stock tick does not belong to the provided list of ticks: {ticks_list}"
    ndf = df[df['ticks'] == tick]
    return ndf

TechStocks = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Create individual DataFrames for each stock
MSFT_df = subdataframe(new_df, 'MSFT', *TechStocks)
ORCL_df = subdataframe(new_df, 'ORCL', *TechStocks)
ADBE_df = subdataframe(new_df, 'ADBE', *TechStocks)
PANW_df = subdataframe(new_df, 'PANW', *TechStocks)
SNPS_df = subdataframe(new_df, 'SNPS', *TechStocks)
CRWD_df = subdataframe(new_df, 'CRWD', *TechStocks)

# Apply the dailyfunc function to each DataFrame
MSFT_df = dailyfunc(MSFT_df)
ORCL_df = dailyfunc(ORCL_df)
ADBE_df = dailyfunc(ADBE_df)
PANW_df = dailyfunc(PANW_df)
SNPS_df = dailyfunc(SNPS_df)
CRWD_df = dailyfunc(CRWD_df)

Mean Price of Each Stock¶

In [30]:
background_gradient = LinearSegmentedColormap.from_list("", ["#212121", "#1A1D23", "#03055B"])
line_colors = ['#FFFF00', '#00BFFF', '#32CD32', '#FF00FF', '#FFA500', '#d62728']  

# Create the figure and axis with dark background
fig, ax = plt.subplots(figsize=(14, 7))
fig.patch.set_facecolor('#1A1D23')  # Set figure background color to match theme
ax.set_facecolor('#212121')  # Set axis background color

# Plot each stock's daily mean price with distinct colors and line widths for visibility on dark theme
ax.plot(MSFT_df['date'], MSFT_df['daily_mean'], label='Microsoft (MSFT)', color=line_colors[0], lw=1.4)
ax.plot(ORCL_df['date'], ORCL_df['daily_mean'], label='Oracle (ORCL)', color=line_colors[1], lw=1.4)
ax.plot(ADBE_df['date'], ADBE_df['daily_mean'], label='Adobe (ADBE)', color=line_colors[2], lw=1.4)
ax.plot(PANW_df['date'], PANW_df['daily_mean'], label='Palo Alto Networks (PANW)', color=line_colors[3], lw=1.4)
ax.plot(SNPS_df['date'], SNPS_df['daily_mean'], label='Synopsys (SNPS)', color=line_colors[4], lw=1.4)
ax.plot(CRWD_df['date'], CRWD_df['daily_mean'], label='CrowdStrike (CRWD)', color=line_colors[5], lw=1.4)

# Format the x-axis to display years only
ax.xaxis.set_major_locator(mdates.YearLocator())  # Place tick at the start of each year
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format to show only the year
plt.xticks(rotation=0, color='white')  # Make tick labels white for dark background

# Add title and labels with improved formatting
ax.set_title("Comparative Analysis of Tech Stock Prices Based on Mean Price", fontsize=16, fontweight='bold', color='white')
ax.set_ylabel("Daily Average Stock Price", fontsize=14, color='white')

# Add legend with a clear background
ax.legend(facecolor='#1A1D23', fontsize="medium", title="Tech Stocks", title_fontsize=13, labelcolor='white', edgecolor='white')

# Add gridlines for better readability, change to a softer color to match the dark theme
ax.grid(True, color='#444444')

# Show the plot
plt.tight_layout()
plt.show()
No description has been provided for this image

Daily Mean Foruma = (Open + Close + High + Low) / 4

Volitatliy Analysis¶

In [31]:
dataframes = [MSFT_df, ORCL_df, ADBE_df, PANW_df, SNPS_df, CRWD_df]
labels = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Set up the plot
plt.figure(figsize=(14, 8))

# Loop through each DataFrame to plot only KDE (no histograms)
for df, label in zip(dataframes, labels):
    # Plot only KDE for each DataFrame's 'daily return' with a specific color palette for differentiation
    sns.kdeplot(df['daily return'], label=label, linewidth=2)

# Add labels and title
plt.title('KDE of Daily Returns for Top 6 Stocks')
plt.xlabel('Daily Return')
plt.ylabel('Density')
plt.legend(title='Stock')

# Show the plot
plt.show()
No description has been provided for this image

This Standardized chart shows the Variations , Here CRWD seems to the most Volatile amoung them ,

  • There is a better way to shwo this with the Box and Whisker Plot , so we could get the idea of the Outliers better
In [32]:
dataframes = [MSFT_df, ORCL_df, ADBE_df, PANW_df, SNPS_df, CRWD_df]
labels = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Create a combined DataFrame for plotting
combined_df = pd.DataFrame()

# Loop through dataframes and calculate standard deviation of 'daily return'
std_devs = {}
for df, label in zip(dataframes, labels):
    # Create a temporary DataFrame with a 'Stock' column
    temp_df = df[['daily return']].copy()
    temp_df['Stock'] = label
    
    # Append to the combined DataFrame
    combined_df = pd.concat([combined_df, temp_df])
    
    # Calculate the standard deviation for this stock and store it
    std_devs[label] = temp_df['daily return'].std()

# Sort stocks by their standard deviation (volatility)
sorted_labels = sorted(std_devs, key=std_devs.get)

# Set up the plot
plt.figure(figsize=(12, 6))

# Create the box plot, sorted by volatility
sns.boxplot(x='Stock', y='daily return', data=combined_df, order=sorted_labels, palette="Set2")

# Add labels and title
plt.title('Box Plot of Daily Returns for Top 6 Stocks (Sorted by Volatility)')
plt.ylabel('Daily Return')

# Show the plot
plt.show()
No description has been provided for this image

Risk Analysis¶

How much value do we put at risk by investing in a particular stock?

Risk analysis is performed by plotting the expected return (mean) against the risk (standard deviation) to show Volatility for each stock in a scatter plot.

In [33]:
means = []
std_devs = []

# Loop through dataframes and calculate mean and standard deviation for each stock
for df in dataframes:
    mean_return = df['daily return'].mean()
    std_dev = df['daily return'].std()
    means.append(mean_return)
    std_devs.append(std_dev)

# Define size and color for scatter plot
area = np.pi * 20  # Size of the scatter plot marker
colors = ['blue', 'green', 'red', 'purple', 'orange', 'brown']  # Colors for each stock

# Create a scatter plot
plt.figure(figsize=(12, 8))

# Plot each stock with a different color and annotate
for mean, std, label, color in zip(means, std_devs, labels, colors):
    plt.scatter(mean, std, s=area, c=color, label=label)
    plt.annotate(label, xy=(mean, std), xytext=(50, 50), textcoords='offset points',
                 ha='right', va='bottom', 
                 arrowprops=dict(arrowstyle='-', color=color, connectionstyle='arc3,rad=-0.3'))

# Add labels, title, and legend
plt.xlabel('Expected Return (Mean)')
plt.ylabel('Risk (Standard Deviation)')
plt.title('Scatter Plot of Risk vs Expected Return for Top 6 Stocks')

# Show the plot
plt.show()
No description has been provided for this image

Moving Average¶

A very basic Measure to smothen out the Noise is using moving averages

  • it's speciality Lies in using it's Golden Cross and Death Cross features in it

So we are going to use comapative comaises and comapre them with each other now

In [34]:
dataframes = [PANW_df, CRWD_df]
labels = ['PANW', 'CRWD']

PANW_df['date'] = pd.to_datetime(PANW_df['date'])
CRWD_df['date'] = pd.to_datetime(CRWD_df['date'])
PANW_df.set_index('date', inplace=True)
CRWD_df.set_index('date', inplace=True)
In [ ]:
 
In [35]:
short_window = 50
long_window = 200

# Loop through companies and calculate moving averages
for company, label in zip([PANW_df, CRWD_df], ['PANW', 'CRWD']):
    company['Short MA'] = company['adj close'].rolling(window=short_window).mean()
    company['Long MA'] = company['adj close'].rolling(window=long_window).mean()
    
    # Detect Golden Cross and Death Cross
    company['Signal'] = 0.0  # Initialize signal column
    company['Signal'] = np.where(company['Short MA'] > company['Long MA'], 1.0, 0.0)
    company['Cross'] = company['Signal'].diff()

# Function to plot stock with Golden Cross and Death Cross
def plot_stock_with_cross(company, name, ax):
    ax.plot(company['adj close'], label='Adjusted Close', alpha=0.6)
    ax.plot(company['Short MA'], label=f'{short_window}-day MA', alpha=0.7)
    ax.plot(company['Long MA'], label=f'{long_window}-day MA', alpha=0.7)
    
    # Mark Golden Cross (1) and Death Cross (-1)
    ax.plot(company[company['Cross'] == 1].index, company['Short MA'][company['Cross'] == 1], '^', markersize=10, color='g', lw=0, label='Golden Cross')
    ax.plot(company[company['Cross'] == -1].index, company['Short MA'][company['Cross'] == -1], 'v', markersize=10, color='r', lw=0, label='Death Cross')
    
    ax.set_title(name)
    ax.legend()

    # Set major ticks to every year (from 2019 to 2024)
    ax.xaxis.set_major_locator(mdates.YearLocator())  # Every year
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format as 'Year'
    
    # Ensure x-axis spans from 2019 to 2024
    ax.set_xlim([pd.Timestamp('2019-01-01'), pd.Timestamp('2024-12-31')])

# Set up plot with subplots for PANW and CRWD
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))

# Plot for PANW and CRWD
plot_stock_with_cross(PANW_df, 'PANW', axes[0])
plot_stock_with_cross(CRWD_df, 'CRWD', axes[1])

plt.tight_layout()
plt.show()
No description has been provided for this image
  • CrowdStrike had faced 2 Death cross one of them is in Year 2024 , I think we have in idea why and when it did that happen

-- We will Analyze the most Recent Event that could Have caused the strike

In [36]:
check = CRWD_df[CRWD_df['Cross'] == -1]
check
Out[36]:
ticks adj close close high low open volume daily return daily_mean co_name Short MA Long MA Signal Cross
date
2022-01-04 CRWD 189.190002 189.190002 197.445007 183.320007 195.889999 6503400.0 -3.420285 191.461254 CrowdStrike Holdings 235.3460 236.554925 0.0 -1.0
2024-09-03 CRWD 266.600006 266.600006 277.510010 265.290009 275.779999 5485800.0 -3.328738 271.295006 CrowdStrike Holdings 299.4642 300.198974 0.0 -1.0
  • It looks like , Our Data is working as Crowdstrike did indeed had a death cross ,
  • Full Article

image-2.png

Market and Equity Analysis of Stocks¶

In [37]:
symbols = ['GOOGL', 'MSFT','WMT','AMZN','BABA','ORCL','SAP','CRM']

holders_data = []
for symbol in symbols:
    stock = yf.Ticker(symbol)
    try:
        institutional_holders = stock.institutional_holders
        holders_data.append({
            'Stock Symbol': symbol,
            'Top 10 Institutional Holders': institutional_holders.head(10)
        })
    except:
        holders_data.append({
            'Stock Symbol': symbol,
            'Top 10 Institutional Holders': None
        })

Holders_data_df = pd.DataFrame(holders_data)
Holders_data_df.set_index('Stock Symbol', inplace=True)
Holders_data_df
Out[37]:
Top 10 Institutional Holders
Stock Symbol
GOOGL Date Reported Ho...
MSFT Date Reported Ho...
WMT Date Reported Ho...
AMZN Date Reported Ho...
BABA Date Reported H...
ORCL Date Reported ...
SAP Date Reported Hold...
CRM Date Reported Holder...

Equity ownership analysis¶

By Top 10 Institutional StakeHolders

In [38]:
from plotly.subplots import make_subplots

def create_interactive_holdings_pie_chart(df):
    # Create a 4x2 subplot grid
    fig = make_subplots(rows=4, cols=2, subplot_titles=df.index, specs=[[{'type':'domain'}, {'type':'domain'}]] * 4)

    for i, (symbol, data) in enumerate(df.iterrows(), 1):
        row = (i - 1) // 2 + 1
        col = (i - 1) % 2 + 1

        if data['Top 10 Institutional Holders'] is not None:
            holders = data['Top 10 Institutional Holders']
            
            # Create a pie chart for each stock
            fig.add_trace(
                go.Pie(
                    labels=holders['Holder'],
                    values=holders['Shares'],
                    name=symbol,
                    textposition='inside',
                    textinfo='percent+label',
                    hoverinfo='label+value+percent',
                    textfont_size=8
                ),
                row=row, col=col
            )

    # Update layout
    fig.update_layout(
        height=2000, 
        width=1400,
        title_text="Top 10 Institutional Holders for Various Stocks",
        showlegend=False
    )

    return fig

# Assuming Holders_data_df is your DataFrame with the data
# Create the plot
fig = create_interactive_holdings_pie_chart(Holders_data_df)

# Show the plot
fig.show()
  • it's quite intresting to note , How these Big investment firms Owns , Very Similar Shares in Both Tech Giants

Next we are Going to do some Analysis , Selecting do a Side by Side Comparision on Some Direct Alternates of Each Other as¶

S&P Index Comp alternate 1 alternate 2 Stock Symbols
Google (Alphabet) Microsoft GOOG/GOOGL, MSFT
Walmart Amazon Alibaba WMT, AMZN, BABA
Oracle SAP Salesforce ORCL, SAP, CRM
In [39]:
symbols = ['GOOGL', 'MSFT','WMT','AMZN','BABA','ORCL','SAP','CRM']

Historic EPS and PE Ratio¶

Obtaining current EPS and PE ratio data from YFinance is straightforward, but finding a way to get it for the last 12 months has been a significant challenge in my research.

  • Either the sources are paid, such as Quandl, Zack, or Alpha Vantage API
  • Or, web scraping is not allowed, leading to forbidden errors

My Approach¶

  • Initially, I planned to scrape the data from the Macrotrends Website using pages like this
  • However, scraping is not allowed, and I encountered a forbidden error , if You Know how to tackle the Fobidden warning , thean you case use the similar Code Below
  • As a worst-case scenario, you can save the tables in a text file and read them manually to extract the data
import pandas as pd

STOCK_URLS = {
    'AAPL': 'https://www.macrotrends.net/stocks/charts/AAPL/apple/pe-ratio',
    'AMD': 'https://www.macrotrends.net/stocks/charts/AMD/amd/pe-ratio',
    'NVDA': 'https://www.macrotrends.net/stocks/charts/NVDA/nvidia/pe-ratio',
    'MSFT': 'https://www.macrotrends.net/stocks/charts/MSFT/microsoft/pe-ratio'
}

# Initialize empty DataFrames
dfs = {stock: pd.DataFrame() for stock in STOCK_URLS.keys()}

# Loop through each stock and URL
for stock, url in STOCK_URLS.items():
    data = pd.read_html(url, skiprows=1)
    df = pd.DataFrame(data[0])
    df = df.columns.to_frame().T.append(df, ignore_index=True)
    df.columns = range(len(df.columns))
    df = df[1:]
    df = df.rename(columns={0: 'Date', 1: 'Price', 2:'EPS', 3:'PE ratio'})
    df['EPS'][1:] = ''
    df.set_index('Date', inplace=True)
    df = df.sort_index()
    df['trend'] = ''
    df['PE ratio'] = df['PE ratio'].astype(float)

In [40]:
file_paths = {
    'AMD': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\AMD PE Ratio 2010-2024.txt",
    'Apple': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\Apple PE Ratio 2010-2024.txt",
    'Microsoft': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\Microsoft PE Ratio 2010-2024.txt",
    'NVIDIA': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\NVIDIA PE Ratio 2010-2024.txt"
}

def clean_eps(value):
    if pd.isna(value) or value == '':
        return None
    return float(value.replace('$', ''))

dfs = {}

for stock, file_path in file_paths.items():
    df = pd.read_csv(file_path, sep=r'\s+', header=None, names=['Date', 'Price', 'EPS', 'PE ratio'])
    
    df['EPS'] = df['EPS'].apply(clean_eps)
    
    df['Price'] = pd.to_numeric(df['Price'], errors='coerce')
    df['PE ratio'] = pd.to_numeric(df['PE ratio'], errors='coerce')
    
    df['Date'] = pd.to_datetime(df['Date'])
    df.set_index('Date', inplace=True)
    
    df = df.iloc[1:]
    
    df.sort_index(inplace=True, ascending=False)
    
    dfs[stock] = df
In [41]:
# Extract the EPS columns from the DataFrames
eps_values = [df['EPS'] for df in dfs.values()]

fig = go.Figure()

# Add lines for each stock
for stock, eps in zip(dfs.keys(), eps_values):
    fig.add_trace(go.Scatter(x=eps.index, y=eps.values, name=stock, line=dict(width=2)))

# Set title and labels
fig.update_layout(
    title='EPS Over Time',
    xaxis_title='Date',
    yaxis_title='EPS',
    plot_bgcolor='#333333',
    paper_bgcolor='#333333',
    font_color='#ffffff'
)

fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)

# Set vibrant line colors
fig.update_traces(marker=dict(color=['#ff69b4', '#33cc33', '#6666ff', '#ffcc00']))

# Show the legend
fig.update_layout(showlegend=True)

fig.update_layout(
    width=600,  # Set the figure width (in pixels)
    height=300,  # Set the figure height (in pixels)
)

# Show the plot
fig.show()
  • Microsoft Apple Continues
In [42]:
pe_ratios = [df['PE ratio'] for df in dfs.values()]
stock_symbols = list(dfs.keys())

# Create the figure
fig = go.Figure()

# Add lines for all stocks, using the stock symbols from the dictionary keys
for i, stock in enumerate(stock_symbols):
    fig.add_trace(go.Scatter(x=pe_ratios[i].index, y=pe_ratios[i].values, name=stock, line=dict(width=2), visible=(i < 2)))

# Set title and labels
fig.update_layout(
    title='PE Ratio Over Time',
    xaxis_title='Date',
    yaxis_title='PE Ratio',
    plot_bgcolor='#333333',
    paper_bgcolor='#333333',
    font_color='#ffffff'
)

# Set vibrant line colors
fig.update_traces(marker=dict(color=['#ff69b4', '#33cc33', '#6666ff', '#ffcc00']))
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
# Add dropdown menu
buttons = [
    dict(label=f'{stock_symbols[0]} & {stock_symbols[3]}', method='update', args=[{'visible': [True, False, False, True]}]),
    dict(label=f'{stock_symbols[1]} & {stock_symbols[2]}', method='update', args=[{'visible': [False, True, True, False]}]),
    dict(label='All 4', method='update', args=[{'visible': [True, True, True, True]}])
]
fig.update_layout(updatemenus=[dict(buttons=buttons, direction='down', showactive=True)])

# Show the legend
fig.update_layout(showlegend=True)

# Show the plot
fig.show()

PE Ratio Insights¶

The ideal PE Ratio is difficult to define in the tech industry, but on average, it falls between 15-16. For mature companies, it can be larger, ranging from 20-25. [1]

From the chart we can see that

  • AMD: In September 2023, AMD had a remarkably All time high PE Ratio of 856.83, accompanied by significant volatility.
  • Nvidia: Nvidia maintained a relatively less variable PE Ratio, which, although still larger than the average, showed more stability.

[1] Source: What is a Good PE Ratio?

Fundamental Analysis with Piotroski F-Score¶

============================================= This script performs fundamental analysis on a list of custom companies using a modified Piotroski F-Score.

Script Description¶

Our robust script utilizes a variation of the Piotroski F-Score, updating the original metrics to incorporate modern financial data and analysis techniques. Piotroski Score

In [43]:
tickers = {
    'Cybersecurity': ['PANW', 'CRWD', 'FTNT'],
    'E-commerce': ['ETSY', 'SHOP', 'W'],
    'Fintech': ['SQ', 'PYPL', 'V'],
    'Software': ['TEAM', 'ZM', 'MSFT'],
    'Gaming': ['EA', 'TTWO', 'AIY.DE'],
    'Automotive': ['RIVN', 'MBLY', 'VFS','TSLA']
}

def get_data(ticker):
    try:
        ticker_obj = yf.Ticker(ticker)
        info = ticker_obj.info
        financials = ticker_obj.financials
        balance_sheet = ticker_obj.balance_sheet
        cash_flow = ticker_obj.cashflow
        
        if financials.empty or balance_sheet.empty or cash_flow.empty:
            print(f"Warning: Missing data for {ticker}")
            return None, None, None, None, None
        
        return ticker_obj, info, financials, balance_sheet, cash_flow
    
    except Exception as e:
        print(f"Error fetching data for {ticker}: {str(e)}")
        return None, None, None, None, None

def calculate_pe(info):
    pe = info.get('trailingPE', 0) or 0
    return min(max(pe, 0), 100)  # Cap PE ratio between 0 and 100

def get_value(df, possible_names, year, ticker):
    for name in possible_names:
        if name in df.index and year in df.columns:
            return df.loc[name, year]
    print(f"Warning: Could not find any of {possible_names} in dataframe for year {year} for {ticker}")
    return 0

def calculate_profitability(financials, balance_sheet, cash_flow, ticker):
    try:
        years = financials.columns
        if len(years) < 2:
            return 0
        
        latest_year, previous_year = years[0], years[1]
        
        net_income = get_value(financials, ['Net Income', 'Net Income Common Stockholders'], latest_year, ticker)
        net_income_py = get_value(financials, ['Net Income', 'Net Income Common Stockholders'], previous_year, ticker)
        
        op_cf = get_value(cash_flow, ['Operating Cash Flow', 'Cash Flow from Operations'], latest_year, ticker)
        
        total_assets = get_value(balance_sheet, ['Total Assets'], latest_year, ticker)
        total_assets_py = get_value(balance_sheet, ['Total Assets'], previous_year, ticker)
        avg_assets = (total_assets + total_assets_py) / 2 if total_assets and total_assets_py else total_assets or total_assets_py or 1
        
        RoA = net_income / avg_assets if avg_assets else 0
        RoA_py = net_income_py / avg_assets if avg_assets else 0
        
        accruals = op_cf / total_assets - RoA if total_assets else 0
        
        # 
        ni_score = min(net_income / 1e9, 2) if net_income > 0 else 0  # Cap at 2 for net income > $2B
        ni_growth_score = min((net_income - net_income_py) / abs(net_income_py) if net_income_py else 0, 1)
        op_cf_score = min(op_cf / 1e9, 2) if op_cf > 0 else 0  # Cap at 2 for operating cash flow > $2B
        roa_score = min(RoA * 10, 2)  # Cap at 2 for ROA > 20%
        roa_growth_score = min((RoA - RoA_py) * 5, 1)  # Cap at 1 for ROA growth > 20%
        accruals_score = min(accruals * 5, 1) if accruals > 0 else 0
        
        return ni_score + ni_growth_score + op_cf_score + roa_score + roa_growth_score + accruals_score
    
    except Exception as e:
        print(f"Error calculating profitability for {ticker}: {str(e)}")
        return 0

def calculate_leverage(balance_sheet, ticker):
    try:
        latest_year = balance_sheet.columns[0]
        
        lt_debt = get_value(balance_sheet, ['Long Term Debt', 'Total Long Term Debt', 'Net Debt'], latest_year, ticker)
        total_assets = get_value(balance_sheet, ['Total Assets'], latest_year, ticker)
        current_assets = get_value(balance_sheet, ['Total Current Assets', 'Current Assets'], latest_year, ticker)
        current_liab = get_value(balance_sheet, ['Total Current Liabilities', 'Current Liabilities'], latest_year, ticker)
        
        debt_ratio = lt_debt / total_assets if total_assets else 0
        current_ratio = current_assets / current_liab if current_liab else 0
        
        # More granular scoring
        debt_ratio_score = max(0, 2 - debt_ratio * 5)  # 0 score for debt_ratio > 0.4, max 2 for debt_ratio = 0
        current_ratio_score = min(current_ratio - 1, 2) if current_ratio > 1 else 0  # Max 2 for current_ratio > 3
        
        return debt_ratio_score + current_ratio_score
    
    except Exception as e:
        print(f"Error calculating leverage for {ticker}: {str(e)}")
        return 0

def calculate_operating_efficiency(financials, balance_sheet, ticker):
    try:
        years = financials.columns
        if len(years) < 2:
            return 0
        
        latest_year, previous_year = years[0], years[1]
        
        gp = get_value(financials, ['Gross Profit'], latest_year, ticker)
        gp_py = get_value(financials, ['Gross Profit'], previous_year, ticker)
        revenue = get_value(financials, ['Total Revenue', 'Revenue'], latest_year, ticker)
        revenue_py = get_value(financials, ['Total Revenue', 'Revenue'], previous_year, ticker)
        total_assets = get_value(balance_sheet, ['Total Assets'], latest_year, ticker)
        total_assets_py = get_value(balance_sheet, ['Total Assets'], previous_year, ticker)

        gm = gp / revenue if revenue else 0
        gm_py = gp_py / revenue_py if revenue_py else 0
        avg_assets = (total_assets + total_assets_py) / 2 if total_assets and total_assets_py else total_assets or total_assets_py or 1
        at = revenue / avg_assets if avg_assets else 0
        at_py = revenue_py / avg_assets if avg_assets else 0
        
        # More granular scoring
        gm_score = min(gm * 2, 2)  # Max 2 for gross margin > 50%
        gm_growth_score = min((gm - gm_py) * 10, 1)  # Max 1 for 10% growth in gross margin
        at_score = min(at, 2)  # Max 2 for asset turnover >= 2
        at_growth_score = min((at - at_py) * 5, 1)  # Max 1 for 20% growth in asset turnover
        
        return gm_score + gm_growth_score + at_score + at_growth_score
    
    except Exception as e:
        print(f"Error calculating operating efficiency for {ticker}: {str(e)}")
        return 0

summary = pd.DataFrame(columns=['Industry', 'Ticker', 'Company Name', 'PE Ratio', 'Profitability', 'Leverage', 'Operating Efficiency'])

for industry, symbols in tickers.items():
    for ticker in symbols:
        ticker_obj, info, financials, balance_sheet, cash_flow = get_data(ticker)
        
        if ticker_obj is None or info is None or financials is None or balance_sheet is None or cash_flow is None:
            print(f"Skipping {ticker} due to missing data")
            continue
        
        pe_ratio = calculate_pe(info)
        profitability = calculate_profitability(financials, balance_sheet, cash_flow, ticker)
        leverage = calculate_leverage(balance_sheet, ticker)
        operating_efficiency = calculate_operating_efficiency(financials, balance_sheet, ticker)
        
        # Fetch the company name from the info dictionary
        company_name = info.get('longName', ticker)
        
        new_row = {
            'Industry': industry,
            'Ticker': ticker,
            'Company Name': company_name,
            'PE Ratio': pe_ratio,
            'Profitability': profitability,
            'Leverage': leverage,
            'Operating Efficiency': operating_efficiency
        }
        
        summary = summary._append(new_row, ignore_index=True)
        
        print(f"{ticker} ({company_name}) added.")

# Calculate Total Score with weights
weights = {'Profitability': 0.4, 'Leverage': 0.3, 'Operating Efficiency': 0.3}
summary['Total Score'] = (summary['Profitability'] * weights['Profitability'] + 
                          summary['Leverage'] * weights['Leverage'] + 
                          summary['Operating Efficiency'] * weights['Operating Efficiency'])

# Normalize scores
for column in ['Profitability', 'Leverage', 'Operating Efficiency', 'Total Score']:
    max_score = summary[column].max()
    if max_score > 0:
        summary[column] = summary[column] / max_score * 10

# Sort by Total Score
summary = summary.sort_values(by='Total Score', ascending=False)

# Save to CSV
summary.to_csv('Summary_with_names.csv', index=False)
print("Summary file with company names added.")
summary
PANW (Palo Alto Networks, Inc.) added.
CRWD (CrowdStrike Holdings, Inc.) added.
FTNT (Fortinet, Inc.) added.
ETSY (Etsy, Inc.) added.
SHOP (Shopify Inc.) added.
W (Wayfair Inc.) added.
SQ (Block, Inc.) added.
PYPL (PayPal Holdings, Inc.) added.
V (Visa Inc.) added.
TEAM (Atlassian Corporation) added.
Warning: Could not find any of ['Long Term Debt', 'Total Long Term Debt', 'Net Debt'] in dataframe for year 2024-01-31 00:00:00 for ZM
ZM (Zoom Video Communications, Inc.) added.
MSFT (Microsoft Corporation) added.
EA (Electronic Arts Inc.) added.
TTWO (Take-Two Interactive Software, Inc.) added.
AIY.DE (Activision Blizzard Inc) added.
RIVN (Rivian Automotive, Inc.) added.
Warning: Could not find any of ['Long Term Debt', 'Total Long Term Debt', 'Net Debt'] in dataframe for year 2023-12-31 00:00:00 for MBLY
MBLY (Mobileye Global Inc.) added.
VFS (VinFast Auto Ltd.) added.
TSLA (Tesla, Inc.) added.
Summary file with company names added.
Out[43]:
Industry Ticker Company Name PE Ratio Profitability Leverage Operating Efficiency Total Score
11 Software MSFT Microsoft Corporation 35.341260 9.050168 4.645531 6.968310 10.0
10 Software ZM Zoom Video Communications, Inc. 25.242857 6.530246 10.0 6.540146 9.676711
2 Cybersecurity FTNT Fortinet, Inc. 49.035503 8.106195 3.768044 9.122103 9.594741
0 Cybersecurity PANW Palo Alto Networks, Inc. 51.263737 10.000000 0.0 7.313343 9.355254
8 Fintech V Visa Inc. 29.747322 8.931555 3.302906 6.186266 9.28916
18 Automotive TSLA Tesla, Inc. 61.352116 8.225444 6.500295 4.307100 9.261078
12 Gaming EA Electronic Arts Inc. 33.703530 7.479406 4.183104 6.801571 8.646036
4 E-commerce SHOP Shopify Inc. 84.080800 4.947972 8.986636 6.936685 8.291835
3 E-commerce ETSY Etsy, Inc. 23.111626 6.829372 3.023663 7.820137 8.072221
7 Fintech PYPL PayPal Holdings, Inc. 19.541264 7.557439 4.255449 3.026382 7.73083
1 Cybersecurity CRWD CrowdStrike Holdings, Inc. 100.000000 4.793815 5.512961 8.633508 7.547236
14 Gaming AIY.DE Activision Blizzard Inc 0.000000 4.930864 8.351623 3.335013 7.132296
9 Software TEAM Atlassian Corporation 0.000000 3.321770 3.082009 10.000000 6.060256
16 Automotive MBLY Mobileye Global Inc. 0.000000 1.671184 10.0 3.858088 5.360779
6 Fintech SQ Block, Inc. 64.537030 1.686429 5.426277 5.973935 4.511236
5 E-commerce W Wayfair Inc. 0.000000 0.755730 0.0 7.544339 2.550857
15 Automotive RIVN Rivian Automotive, Inc. 0.000000 -3.399105 6.698802 3.356351 0.43888
13 Gaming TTWO Take-Two Interactive Software, Inc. 0.000000 -6.862910 1.870831 3.346543 -3.633858
17 Automotive VFS VinFast Auto Ltd. 0.000000 -6.735669 1.908362 2.351831 -3.790046
  • It is Prominent that many of these top Companies , are Pass out most the Metrics we have set easily as they are proftable and are known Industry Leaders so maybe we should be using such Analysis on small to mid size companies
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